import os
import sys
import importlib
import json
import numpy as np
import pandas as pd
import scipy
import scipy.ndimage as snd
import skimage
import uuid
from matplotlib import pyplot as plt
import matplotlib as mpl
import cv2
import plotly
import plotly.express as px
import plotly.graph_objects as go
if os.getcwd().split("/")[-1] == "notebooks": # if cwd is located where this file is
os.chdir("../..") # go two folders upward (the if statement prevents error if cell is rerun)
directory_path = os.path.abspath(os.path.join("src"))
if directory_path not in sys.path:
sys.path.append(directory_path)
import src.EyeTraumaAnalysis
from src.EyeTraumaAnalysis import calculate_roc
cwd: C:\Users\ethan\PycharmProjects\EyeTraumaAnalysis\src\notebooks
Traceback (most recent call last):
File "C:\Users\ethan\PycharmProjects\EyeTraumaAnalysis\src\EyeTraumaAnalysis\main.py", line 19, in <module>
li_df = pd.read_excel(prepath+"data/01_raw/data_li.xlsx", dtype={"centerX":float, "centerY":float})
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\util\_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\util\_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\excel\_base.py", line 482, in read_excel
io = ExcelFile(io, storage_options=storage_options, engine=engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\excel\_base.py", line 1652, in __init__
ext = inspect_excel_format(
^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\excel\_base.py", line 1525, in inspect_excel_format
with get_handle(
^^^^^^^^^^^
File "C:\Users\ethan\AppData\Local\Programs\Python\Python311\Lib\site-packages\pandas\io\common.py", line 865, in get_handle
handle = open(handle, ioargs.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'data/01_raw/data_li.xlsx'
os.getcwd()
'C:\\Users\\ethan\\PycharmProjects\\EyeTraumaAnalysis\\src\\notebooks'
importlib.reload(src.EyeTraumaAnalysis);
# Load metrics
all_metrics = pd.read_pickle("C:/Users/ethan/PycharmProjects/EyeTraumaAnalysis/data/03_first_25percent_metrics/color_and_spatial_metrics" + ".pkl")
all_metrics_flat = pd.read_pickle("C:/Users/ethan/PycharmProjects/EyeTraumaAnalysis/data/03_first_25percent_metrics/color_and_spatial_metrics_flat" + ".pkl")
all_metrics_agg = pd.read_pickle("C:/Users/ethan/PycharmProjects/EyeTraumaAnalysis/data/03_first_25percent_metrics/color_and_spatial_metrics_agg" + ".pkl")
importlib.reload(src.EyeTraumaAnalysis.kmeans)
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat["Ranks-Color-Center-S"], true_value="True")
auc, comparator
(0.6346656742504544, '≤')
notebook_name = "4c_kmeans_rocs"
def get_path_to_save(
plot_props:dict=None, file_prefix="",
save_filename:str=None, save_in_subfolder:str=None, extension="jpg", dot=".", create_folder_if_necessary=True):
replace_characters = {
"$": "",
"\\frac":"",
"\\mathrm":"",
"\\left(":"(",
"\\right)":")",
"\\left[":"[",
"\\right]":"]",
"\\": "",
"/":"-",
"{": "(",
"}": ")",
"<":"",
">":"",
"?":"",
"_":"",
"^":"",
"*":"",
"!":"",
":":"-",
"|":"-",
".":"_",
}
# define save_filename based on plot_props
if save_filename is None:
save_filename = "unnamed"
save_path = ["outputs", notebook_name,]
if save_in_subfolder is not None:
if isinstance(save_in_subfolder, (list, tuple, set, np.ndarray) ):
save_path.append(**save_in_subfolder)
else: # should be a string then
save_path.append(save_in_subfolder)
save_path = os.path.join(*save_path)
if not os.path.exists(save_path) and create_folder_if_necessary:
os.makedirs(save_path)
return os.path.join(save_path, file_prefix+save_filename+dot+extension)
def save_plotly_figure(fig: plotly.graph_objs.Figure,
title: str,
animated=False,
scale=None,
save_in_subfolder:str=None,
extensions=None
):
if scale is None:
scale = 4
if extensions is None:
extensions = ["html"]
if not animated:
# options = ['png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json']
extensions += ["png","pdf"]
extensions = ["html"] # override due to png saving error
for extension in extensions:
try:
if extension in ["htm","html"]:
#fig.update_layout(title=dict(visible=False))
# fig.write_html( get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension),
# full_html=True, include_plotlyjs="directory" )
fig.write_html(f"C:/Users/ethan/PycharmProjects/EyeTraumaAnalysis/outputs/RSY_4/{title}.{extension}")
else:
#if extension == "png":
# fig.update_layout(title=dict(visible=False))
fig.write_image(get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension), scale=scale)
except ValueError as exc:
import traceback
traceback.print_exception(exc)
# def save_plotly_figure(fig: plotly.graph_objs.Figure, title: str, directory="outputs/kmeans-descriptive-subsets/"):
# return
# fig.write_image(os.path.join(directory, title + ".png"))
# fig.write_html( os.path.join(directory, title + ".html"),
# full_html=True, include_plotlyjs="directory" )
# default_plotly_save_scale = 4
# notebook_name = "kmeans-basic-rocs"
#
# def get_path_to_save(
# plot_props:dict=None, file_prefix="",
# save_filename:str=None, save_in_subfolder:str=None, extension="jpg", dot=".", create_folder_if_necessary=True):
# replace_characters = {
# "$": "",
# "\\frac":"",
# "\\mathrm":"",
# "\\left(":"(",
# "\\right)":")",
# "\\left[":"[",
# "\\right]":"]",
# "\\": "",
# "/":"-",
# "{": "(",
# "}": ")",
# "<":"",
# ">":"",
# "?":"",
# "_":"",
# "^":"",
# "*":"",
# "!":"",
# ":":"-",
# "|":"-",
# ".":"_",
# }
# # define save_filename based on plot_props
# if save_filename is None:
# save_filename = "unnamed"
#
# save_path = ["outputs", notebook_name,]
# if save_in_subfolder is not None:
# if isinstance(save_in_subfolder, (list, tuple, set, np.ndarray) ):
# save_path.append(**save_in_subfolder)
# else: # should be a string then
# save_path.append(save_in_subfolder)
# save_path = os.path.join(*save_path)
#
# if not os.path.exists(save_path) and create_folder_if_necessary:
# os.makedirs(save_path)
# return os.path.join(save_path, file_prefix+save_filename+dot+extension)
#
# def save_plotly_figure(fig: plotly.graph_objs.Figure,
# title: str,
# animated=False,
# scale=None,
# save_in_subfolder:str=None,
# extensions=None
# ):
# if scale is None:
# scale = default_plotly_save_scale
# if extensions is None:
# extensions = ["html"]
# if not animated:
# # options = ['png', 'jpg', 'jpeg', 'webp', 'svg', 'pdf', 'eps', 'json']
# extensions += ["png","pdf"]
#
# for extension in extensions:
# try:
# if extension in ["htm","html"]:
# #fig.update_layout(title=dict(visible=False))
# fig.write_html( get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension),
# full_html=True, include_plotlyjs="directory" )
# else:
# #if extension == "png":
# # fig.update_layout(title=dict(visible=False))
# fig.write_image(get_path_to_save(save_filename=title, save_in_subfolder=save_in_subfolder, extension=extension), scale=scale)
# except ValueError as exc:
# import traceback
# traceback.print_exception(exc)
color_discrete_map = {
"True": px.colors.qualitative.Plotly[2], # green
"Maybe": px.colors.qualitative.Plotly[0], # blue
"False": px.colors.qualitative.Plotly[1], # red
}
pattern_shape_map = {}
category_orders = {
"Labels-Value": ["False", "Maybe", "True"],
"facet_col": [False, True],
"facet_row": [False, True],
}
# This is only the start of var_labels. It will be added to programmatically later
var_labels = {
"Labels-Value": "Conjunctiva cluster",
"Values-Color-Center-H": "Center H",
"Values-Color-Center-S": "Center S",
"Values-Color-Center-V": "Center V",
"Values-Color-Range-H": "Range H",
"Values-Color-Range-S": "Range S",
"Values-Color-Range-V": "Range V",
"Values-Location-Mean-x": "Mean x",
"Values-Location-Mean-y": "Mean y",
"Values-Location-SD-x": "SD x",
"Values-Location-SD-y": "SD y",
}
var_labels_copy = var_labels.copy()
suffixes = ["-H","-x"]
for var_label_key in var_labels_copy:
for suffix in suffixes:
if var_label_key.endswith(suffix):
sep = suffix[:1] # should be "-"
suffix_letter = suffix[1:] # should be "-H" or "-x"
# Get name up to suffix letter e.g. "Values-Color-Center-"
var_label_key_prefix = var_label_key[0:-len(suffix_letter)]
# Get all possible suffixes for the prefix i.e. "H", "S", "V"
suffix_letter_options = [var_label_key[len(var_label_key_prefix):] for var_label_key in var_labels_copy
if var_label_key.startswith(var_label_key_prefix)]
combined_suffix_letters = "".join(suffix_letter_options)
# Get combined value
var_label_val_prefix = var_labels[var_label_key_prefix + suffix_letter][:-len(suffix_letter)]
combined_var_label_key = var_label_key_prefix + combined_suffix_letters
combined_var_label_val = var_label_val_prefix + combined_suffix_letters
var_labels[combined_var_label_key] = combined_var_label_val
# Add labels for ranks
var_labels_copy = var_labels.copy()
for var_label_key in var_labels_copy:
if var_label_key.startswith("Values-"):
var_label_key_suffix = var_label_key.split("Values-",maxsplit=1)[-1]
var_labels[f"Ranks-{var_label_key_suffix}"] = var_labels[var_label_key] + " (Rank)"
# Add labels
for var_label_key in all_metrics_flat.columns:
for comparator in [">","<"]:
if comparator in var_label_key:
stem, comparison = var_label_key.split(comparator, maxsplit=1)
if stem in var_labels:
var_labels[var_label_key] = \
(var_labels[stem] + comparator + comparison).replace(">=","≥").replace("<=","≤")
else:
print(var_label_key, stem)
print(var_labels_copy)
raise KeyError
#point_hover_data = ["Values-Color-Center-HSV","Ranks-Color-Center-HSV",
# "Values-Location-Mean-xy","Ranks-Location-Mean-xy",
# "Values-Location-SD-xy","Ranks-Location-SD-xy"]
point_hover_data = {
"Values-Color-Center-H": False,
"Values-Color-Center-S": False,
"Values-Color-Center-V": False,
"Ranks-Color-Center-H": False,
"Ranks-Color-Center-S": False,
"Ranks-Color-Center-V": False,
"Values-Color-Center-HSV":True,
"Ranks-Color-Center-HSV":True,
"Values-Location-Mean-xy":True,
"Ranks-Location-Mean-xy":True,
"Values-Location-SD-xy":True,
"Ranks-Location-SD-xy":True,
}
roc_hover_data = {
"sensitivity":":0.2%",
"specificity":":0.2%",
#"1-specificity":":0.2%",
"threshold":True
}
plotly_template = "plotly_dark" #"simple_white"
def customize_roc_curve(fig: plotly.graph_objs.Figure, add_reference_line=True):
if add_reference_line:
fig.add_shape(type="line", line=dict(dash="dash", width=2), x0=1, y0=0, x1=0, y1=1)
fig.update_layout(
template="simple_white", title=title,
font=dict(
family="Arial",
size=16,
color="black",
),
xaxis=dict(
zeroline=True,
range=[1,0], # reversed range. Alternatively, fig.update_xaxes(autorange="reversed")
showgrid=True,
title="Specificity (reversed)",
nticks=20,
mirror="ticks",
gridcolor="#DDD",
showspikes=True, spikemode="across", spikethickness=2, spikedash="solid"
),
yaxis=dict(
zeroline=True,
range=[0,1],
showgrid=True,
title="Sensitivity",
nticks=20,
mirror="ticks",
gridcolor="#DDD",
showspikes=True, spikemode="across", spikethickness=2, spikedash="solid"
),
legend=dict(
yanchor="bottom",
y=0.01,
xanchor="right",
x=0.99,
bordercolor="Black", #font_size=16,
borderwidth=2,
),
autosize=False,
)
def add_threshold_annotations(fig: plotly.graph_objs.Figure, roc_df, comparator: str, color=None):
if color is None:
color = fig.data[0].line.color
for ind, row in roc_df.iterrows():
if ind==0 or ind==roc_df.shape[0] - 1: # if first or last row, then skip
continue
fig.add_annotation(
x=roc_df.loc[ind, "specificity"],
y=roc_df.loc[ind, "sensitivity"],
text=f"{comparator}{roc_df.loc[ind, 'threshold']}",
arrowhead=2,
font=dict(color=color),
#arrowcolor=fig.data[0].line.color,
bgcolor="#eee", bordercolor="#000", opacity=0.8,
)
def add_auc_annotation(fig: plotly.graph_objs.Figure, auc):
fig.add_annotation(
xanchor="right",yanchor="bottom",
x=0.01, y=0.01, borderpad=5,
text=f"<b>AUC: {auc:.3f}</b>",
font=dict(size=16),
showarrow=False,
opacity=0.8,
bgcolor="#FFF", bordercolor="#000",
borderwidth=2,
)
title = "HSV ROC curve - V rank - 3-24-2023"
predictor_name = "Ranks-Color-Center-V"
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
fig = px.area(roc_df,
x="specificity", y="sensitivity",
hover_data=roc_hover_data, markers=True, title=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
category_orders=category_orders, labels=var_labels, template=plotly_template,
)
add_threshold_annotations(fig, roc_df, comparator)
add_auc_annotation(fig, auc)
customize_roc_curve(fig)
fig.update_layout(font=dict(family="Arial",size=16,)) #, margin=dict(l=20, r=20, t=20, b=20)
fig.show()
save_plotly_figure(fig, title)
for title, predictor_name in zip(
["HSV ROC curve - H rank", "HSV ROC curve - S rank", "HSV ROC curve - V rank"],
["Ranks-Color-Center-H", "Ranks-Color-Center-S", "Ranks-Color-Center-V"]):
roc_df, auc, _ = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
fig = px.area(roc_df,
x="specificity", y="sensitivity",
hover_data=roc_hover_data, markers=True,
range_y=[0, 1], range_x=[0, 1], title=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
category_orders=category_orders, labels=var_labels, template=plotly_template,
)
add_threshold_annotations(fig, roc_df, comparator)
add_auc_annotation(fig, auc)
customize_roc_curve(fig)
fig.show()
save_plotly_figure(fig, title)
for title, predictor_name in zip(
["HSV ROC curve - H", "HSV ROC curve - S", "HSV ROC curve - V"],
["Values-Color-Center-H", "Values-Color-Center-S", "Values-Color-Center-V"]):
roc_df, auc, _ = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
fig = px.area(roc_df,
x="specificity", y="sensitivity",
hover_data=roc_hover_data, markers=True,
range_y=[0, 1], range_x=[0, 1], title=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
category_orders=category_orders, labels=var_labels, template=plotly_template,
)
customize_roc_curve(fig)
add_auc_annotation(fig, auc)
fig.show()
save_plotly_figure(fig, title)
title = "HSV ROC Curve - HSV centers - 3-24-2023"
predictor_names = ["Values-Color-Center-H", "Values-Color-Center-S", "Values-Color-Center-V"]
fig = go.Figure()
# Add diagonal random chance reference line
fig.add_shape(type="line", line=dict(dash="dot",width=2), x0=1, y0=0, x1=0, y1=1)
for ind, predictor_name in enumerate(predictor_names):
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
comparator = comparator.replace(">=","≥").replace("<=","≤")
youden = roc_df["specificity"] + roc_df["sensitivity"] -1
max_youden_loc = youden.argmax()
color = px.colors.qualitative.Safe[ind]
fig.add_trace(go.Scatter(
x=roc_df["specificity"], y=roc_df["sensitivity"],
mode="lines+markers", opacity=0.75,
name=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}"
))
print(roc_df["specificity"][max_youden_loc], roc_df["sensitivity"][max_youden_loc],)
fig.add_annotation(
x=roc_df["specificity"][max_youden_loc], y=roc_df["sensitivity"][max_youden_loc],
text=f"At {comparator}{roc_df['threshold'][max_youden_loc]}",
showarrow=True,
font=dict(color=color),
opacity=0.8,
bgcolor="#FFF", bordercolor="#000",
arrowwidth=2,
arrowhead=1)
customize_roc_curve(fig)
fig.show()
save_plotly_figure(fig, title)
0.20016680567139283 0.9123505976095617 0.609674728940784 0.6972111553784861 0.6371976647206005 0.8247011952191236
title = "HSV ROC Curve - HSV centers (rank) - 3-24-2023"
predictor_names = ["Ranks-Color-Center-H", "Ranks-Color-Center-S", "Ranks-Color-Center-V"]
fig = go.Figure()
# Add diagonal random chance reference line
fig.add_shape(type="line", line=dict(dash="dot",width=2), x0=1, y0=0, x1=0, y1=1)
textpositions = ["bottom right", "top right", "top left",]
for ind, predictor_name in enumerate(predictor_names):
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
comparator = comparator.replace(">=","≥").replace("<=","≤")
youden = roc_df["specificity"] + roc_df["sensitivity"] -1
max_youden_loc = youden.argmax()
color = px.colors.qualitative.Safe[ind]
fig.add_trace(go.Scatter(
x=roc_df["specificity"], y=roc_df["sensitivity"],
mode="lines+markers+text", opacity=0.75,
#text=roc_df["threshold"].apply( (comparator+"{:}").format), textposition=textpositions[ind],
name=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
marker=dict(color=color),
))
fig.add_annotation(
x=roc_df["specificity"][max_youden_loc], y=roc_df["sensitivity"][max_youden_loc],
text=f"At {comparator}{roc_df['threshold'][max_youden_loc]}",
showarrow=True,
font=dict(color=color),
#arrowcolor=color,
bgcolor="#FFF", bordercolor="#000",
opacity=0.8,
arrowwidth=2,
arrowhead=2)
max_youden_loc = 1
fig.add_annotation(
x=roc_df["specificity"][max_youden_loc], y=roc_df["sensitivity"][max_youden_loc],
text=f"At {comparator}{roc_df['threshold'][max_youden_loc]}",
showarrow=True,
font=dict(color=color),
#arrowcolor=color,
bgcolor="#FFF", bordercolor="#000",
opacity=0.8,
arrowwidth=2,
arrowhead=2)
customize_roc_curve(fig)
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()
save_plotly_figure(fig, title)
title = "Loc ROC Curve - xy value - 3-24-2023"
predictor_names = ["Values-Location-Mean-x", "Values-Location-Mean-y",
"Values-Location-SD-x", "Values-Location-SD-y"]
fig = go.Figure()
# Add diagonal random chance reference line
fig.add_shape(type="line", line=dict(dash="dot",width=2), x0=1, y0=0, x1=0, y1=1)
for ind, predictor_name in enumerate(predictor_names):
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
comparator = comparator.replace(">=","≥").replace("<=","≤")
youden = roc_df["specificity"] + roc_df["sensitivity"] -1
max_youden_loc = youden.argmax()
max_youden = youden[max_youden_loc]
color = px.colors.qualitative.Safe[ind]
print(f"""At max youden ({max_youden:.1f}), thresh: {comparator}{roc_df['threshold'][max_youden_loc]:.1%}, ss: {roc_df['sensitivity'][max_youden_loc]:.1%}, sp: {roc_df['specificity'][max_youden_loc]:.1%}""")
fig.add_trace(go.Scatter(
x=roc_df["specificity"], y=roc_df["sensitivity"],
mode="lines+markers", opacity=0.75,
name=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
marker=dict(color=color),
))
fig.add_annotation(
x=roc_df["specificity"][max_youden_loc], y=roc_df["sensitivity"][max_youden_loc],
text=f"At {comparator}{roc_df['threshold'][max_youden_loc]:.1%}",
showarrow=True,
font=dict(color=color),
opacity=0.8,
bgcolor="#FFF", bordercolor="#000",
arrowwidth=2,
arrowhead=1)
customize_roc_curve(fig)
fig.show()
save_plotly_figure(fig, title)
At max youden (0.1), thresh: ≤45.6%, ss: 37.1%, sp: 76.2% At max youden (0.3), thresh: ≥38.2%, ss: 83.3%, sp: 50.3% At max youden (-0.0), thresh: ≥21.3%, ss: 35.9%, sp: 62.5% At max youden (0.0), thresh: ≥29.5%, ss: 17.5%, sp: 86.2%
formula = "{}"
formula.format(1), formula.format(2.0), formula.format(3.1), formula.format(3.14159265358), formula.format(10.1)
('1', '2.0', '3.1', '3.14159265358', '10.1')
title = "Loc ROC Curve - xy (rank) - 3-24-2023"
predictor_names = ["Ranks-Location-Mean-x", "Ranks-Location-Mean-y",
"Ranks-Location-SD-x", "Ranks-Location-SD-y"]
fig = go.Figure()
# Add diagonal random chance reference line
fig.add_shape(type="line", line=dict(dash="dot",width=2), x0=1, y0=0, x1=0, y1=1)
for ind, predictor_name in enumerate(predictor_names):
roc_df, auc, comparator = calculate_roc(all_metrics_flat["Labels-Value"],
all_metrics_flat[predictor_name], true_value="True")
comparator = comparator.replace(">=","≥").replace("<=","≤")
youden = roc_df["specificity"] + roc_df["sensitivity"] -1
max_youden_loc = youden.argmax()
color = px.colors.qualitative.Safe[ind]
fig.add_trace(go.Scatter(
x=roc_df["specificity"], y=roc_df["sensitivity"],
mode="lines+markers+text", opacity=0.75,
#text=roc_df["threshold"].apply((comparator+"{:}").format), textposition="bottom right",
name=f"{var_labels[predictor_name]}, AUC: {auc:0.3f}",
marker=dict(color=color),
customdata=np.stack((np.round(roc_df["threshold"],2),roc_df["threshold"]),axis=-1),
hovertemplate =
"<b>Specificity</b>: %{x:.2%}" + "<br>" +
"<b>Sensitivity</b>: %{y:.2%}" + "<br>" +
"<b>Threshold</b>: " + comparator + "%{customdata[0]}"
))
fig.add_annotation(
x=roc_df["specificity"][max_youden_loc], y=roc_df["sensitivity"][max_youden_loc],
text=f"At {comparator}{roc_df['threshold'][max_youden_loc]}",
showarrow=True,
font=dict(color=color),
#arrowcolor=color,
bgcolor="#FFF", bordercolor="#000",
opacity=0.8,
arrowwidth=2,
arrowhead=2)
customize_roc_curve(fig, add_reference_line=False)
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()
save_plotly_figure(fig, title)